ABSTRACT

This paper describes a study on the development of a human face verification system by merely using template matching (TM) as the main verification engine. In contrast to common face recognition techniques, our approach for the identity verification (face recognition) consists of matching the facial features extracted from the detected face. These facial features namely left eye, right eye and mouth regions; are detected using a system known as EMoTracker. As TM is sensitive to lighting, this study considered different type of lighting directions and similarity computation in TM. Three types of TM functions are evaluated in this paper: Sum of Squared Difference, Cross Correlation and Correlation Coefficient. In the experiments, YaleB Database is used. Based on the observation results, using Correlation Coefficient is shown to be the most reliable similarity computation to handle different lighting conditions using TM.